英伟达Developer Technology Engineer - AI
任职要求
• A degree from university in an engineering or computer science related discipline (BS; MS or PhD preferred). • 2+ working experience is required. • Strong knowledge of C/C++ and/or Fortran. • Deep knowledge of software design, programming techniques, and algorithms. • Expert knowledge of LLM training/inference optimization, including but not limited to development and optimization experience in distributed training/inference, NCCL, NVSHMEM, IB, RoCE, etc. • Strong mathematical fundamentals, including linear algebra and numerical methods. • Experience with parallel programming, ideally CUDA C/C++ and OpenACC. • Good communication and organization skills, with a logical approach to problem solving, good time management, and task prioritization skills. With highly competitive salaries and a comprehensive benefits package,
工作职责
• Working directly with key application developers (especially LLM) to understand the current and future problems they are solving, creating and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through both library development and direct contribution to the applications. This includes training and inference optimization for large language models, directly contributing to frameworks such as Megatron and TRTLLM, SGLang, vLLM... • Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms, and programming models, including by investigating impact on application performance and developer productivity. • Engaging in deep optimization of high-performance operators, involving but not limited to CUDA deep optimization, instruction and compiler optimization. These optimizations will directly support customers or be integrated into products like cuDNN, cuBLAS, and CUTLASS... • Some travel is required for conferences and for on-site visits with developers.
• Study and develop cutting-edge techniques in deep learning, graphs, machine learning, and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures. • Work directly with key customers to understand the current and future problems they are solving and provide the best AI solutions using GPUs. • Collaborate closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms, and programming models.
• Study and develop cutting-edge techniques in deep learning, graphs, machine learning, and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures. • Working on key applications (e.g., LLM training and inference) to understand the current and future problems they are solving, crafting and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through both library development and direct contribution to the applications. • Collaborating closely with diverse groups at NVIDIA such as the architecture, research, libraries, tools, and system software teams to influence the design of next-generation architectures, software platforms, and programming models, by investigating the impact on application performance and developer efficiency. • Travel for on-site visits with developers and to conferences.
We are looking for a Generative AI Intern Engineer to join the NVIDIA Developer Technology group (Devtech) and work with a team of experienced engineers on innovative uses of AI for games and content creation. The Devtech team works with NVIDIA researchers and leading game developers to bring cutting edge AI research from across NVIDIA and the industry to gamers and 3D professionals in high performance packages such as real-time inferenced graphics, physics and animations. What you’ll be doing: • Research and implement innovative generative AI algorithms for game engines and authoring tools, including real-time neural graphics, physics based animation and diffusion models. • Develop neural graphics, animation and physics models and maintain open-source projects for both game-making and user runtimes. Integrate them into mainstream game engines and DCC tools. • Use various optimization techniques, such as tensor fusion and quantization, to fit the AI models onto user devices and maximize the performance of inference for real-time gaming. • Collaborate with game developers on optimizations and improvements for specific GenAI applications. • Interact closely with the architecture and driver teams at NVIDIA in ensuring the best possible experience on current generation hardware, and on determining trends and features for next generation architectures.